Forward Deployed Engineer

Role Overview

• Implement Fuse end-to-end full stack features across our platform: APIs and services on the backend, and customer-facing dashboards and operator tools on the frontend.
• Model and configure each client's lending domain in Fuse: loan types, underwriting rules, debt adjustment rules, task flows, adverse actions, and email/e-sign templates.
• Build and maintain client-specific automations and workflows that orchestrate the end-to-end loan lifecycle, integrating with credit bureaus, KYC providers, banking rails, and document services.
• Ship AI-powered features into production: LLM-based automations, classification and extraction pipelines, agentic workflows, and tool-using agents (e.g., MCP-style tool integrations) that operate on real customer data.
• Own features end-to-end: scoping with implementation, testing, deployment, monitoring, and iteration based on production signals.
• Write clean, well-tested, observable code and contribute to our shared standards on code quality, reliability, and security.

Requirements

• 3+ years of professional experience as a software engineer, ideally in a full-stack role.
• Strong proficiency with a modern backend stack (Node.js/TypeScript, Python, Go, or similar) and experience designing and consuming REST/GraphQL APIs.
• Solid frontend experience with React (Next.js a plus), TypeScript, and modern state management; able to ship polished, responsive UIs.
• Comfortable working with relational databases (PostgreSQL), writing non-trivial queries, and thinking about data modeling for transactional systems.
• Experience deploying services to the cloud (AWS, GCP, or similar), with a working understanding of CI/CD, observability, and basic operational practices.
• Comfortable working in English, both written and spoken; able to collaborate asynchronously with a distributed team.
• Hands-on experience integrating LLMs (OpenAI, Anthropic, or equivalent) into production systems not just prototypes or notebooks.
• Comfort with prompt engineering, structured outputs, tool/function calling, evaluation, and guardrails for AI-driven features.
• Experience with at least one of: retrieval-augmented generation (RAG), agentic workflows, MCP servers/clients, vector search, or LLM observability.
• Pragmatic understanding of when to use AI versus deterministic logic, including cost, latency, reliability, and risk trade-offs in regulated environments such as lending.
• Daily use of AI-assisted development tools (Claude Code, Cursor, Copilot, or similar) with a clear point of view on where they help and where they hurt.

Tech Stack

• AWS
• Cloud
• Google Cloud Platform
• GraphQL
• JavaScript
• Next.js
• Node.js
• Postgres
• Python
• React
• Ruby on Rails
• TypeScript
• Go

Benefits

• Competitive compensation
• Full AI tooling stack covered
• Remote-first culture with strong autonomy
• Real impact on the financial infrastructure used by credit unions and lenders across the U.S.
• Direct impact on the product roadmap and on how lending gets done in the region.